Correlate application performance with hardware performance via heatmap

ABSTRACT

In one aspect, a system for correlating application performance data with hardware performance data via heat maps is disclosed. The system includes: a processor; a memory; and one or more modules stored in the memory and executable by a processor to perform operations. The operations include: receive data associated with monitored applications and hardware; identify application performance data and hardware performance data from the received data; generate interactive heat maps of the application performance data and interactive heat maps of the hardware performance data; provide a user interface for displaying the generated heat maps; and display, through the user interface, the generated heat maps of the application performance data and the generated heat maps of the hardware performance data using a common time scale.

BACKGROUND

In pursuit of the highest level of service performance and userexperience, companies around the world are engaging in digitaltransformation by enhancing investments in digital technology andinformation technology (IT) services. By leveraging the global system ofinterconnected computer networks afforded by the Internet and the WorldWide Web, companies are able to provide ever increasing web services totheir clients. The web services may be provided by a web applicationwhich uses multiple services and applications to handle a giventransaction. The applications may be distributed over severalinterconnected machines, such as servers, making the topology of themachines that provide the service more difficult to track and monitor.In addition, identifying and visualizing correlation between applicationperformance and hardware performance is non-trivial.

SUMMARY

Examples of implementations of correlating application performance withhard ware performance via heat maps are disclosed. Specifically, thedisclosed techniques for correlating application performance andhardware performance via heat maps can be used to identify and visualizethe correlation for a user to understand the correlation in a singleview. The disclosed techniques for correlating application performanceand hardware performance via heat maps can enable a user to easilyunderstand the relationships between specific application performanceand hardware performance in a single view.

In one aspect, a system for correlating application performance datawith hardware performance data via heat maps is disclosed. The systemincludes: a processor; a memory; and one or more modules stored in thememory and executable by a processor to perform operations. Theoperations include: receive data associated with monitored applicationsand hardware; identify application performance data and hardwareperformance data from the received data; generate interactive heat mapsof the application performance data and interactive heat maps of thehardware performance data; provide a user interface for displaying thegenerated heat maps; and display, through the user interface, thegenerated heat maps of the application performance data and thegenerated heat maps of the hardware performance data using a common timescale.

The system can be implemented in various ways to include one or more ofthe following features. For example, when generating the interactiveheat maps, the one or more modules can be executable to performoperations including: generate interactive heat maps for a specificapplication performance data metric or a specific hardware performancemetric or both a specific application performance data metric and aspecific hardware performance metric. The specific applicationperformance data metric can include call counts. The specific hardwareperformance data metric can include CPU usage percentage, memory usagepercentage, or both CPU usage percentage and memory usage percentage.The one or more modules can be executable to perform operationsincluding: receive a selection of a block from the applicationperformance heat maps or a block from the hardware performance heatmaps; and responsive to the received selection of the block, display oneor more blocks from the hardware performance heat maps or one or moreblocks from the application performance heat maps, depending on thereceived selection of the block, to visually indicate a correlation. Theone or more modules can be executable to visually indicate thecorrelation by performing operations including: apply highlights,colors, shading, brightness, transparency, or text to indicate thecorrelation between selected block and the correlated block or blocks.The one or more modules can be executable to perform operationsincluding: display additional information to identify the selected blockand the correlated block or blocks. The one or more modules can beexecutable to perform operations including: receive a selection ofmultiple blocks from the application performance heat maps or multipleblocks from the hardware performance heat maps; responsive to thereceived selection of the multiple blocks, identify a common entity orentities that correlate to all of the selected multiple blocks; anddisplay one or more blocks from the hardware performance heat maps orone or more blocks from the application performance heat maps, dependingon the received selection of the multiple blocks, that represent theidentified common entity or entities.

In another aspect, a method for correlating application performance datawith hardware performance data is disclosed. The method includes:receiving data associated with monitored applications and hardware;identifying application performance data and hardware performance datafrom the received data; generating interactive heat maps of theapplication performance data and interactive heat maps of the hardwareperformance data; providing a user interface for displaying thegenerated heat maps; and displaying, through the user interface, thegenerated heat maps of the application performance data and thegenerated heat maps of the hardware performance data using a common timescale.

The method can be implemented in various ways to include one or more ofthe following features. For example, generating the interactive heatmaps can include: generating interactive heat maps for a specificapplication performance data metric or a specific hardware performancemetric or both a specific application performance data metric and aspecific hardware performance metric. The specific applicationperformance data metric can include call counts. The specific hardwareperformance data metric can include CPU usage percentage, memory usagepercentage, or both CPU usage percentage and memory usage percentage.The method can include receiving a selection of a block from theapplication performance heat maps or a block from the hardwareperformance heat maps; and responsive to the received selection of theblock, displaying one or more blocks from the hardware performance heatmaps or one or more blocks from the application performance heat maps,depending on the received selection of the block, to visually indicate acorrelation. The method can include applying highlights, colors,shading, brightness, transparency, or text to indicate the correlationbetween selected block and the correlated block or blocks. The methodcan include: displaying additional information to identify the selectedblock and the correlated block or blocks. The method can include:receiving a selection of multiple blocks from the applicationperformance heat maps or multiple blocks from the hardware performanceheat maps; responsive to the received selection of the multiple blocks,identifying a common entity or entities that correlate to all of theselected multiple blocks; and displaying one or more blocks from thehardware performance heat maps or one or more blocks from theapplication performance heat maps, depending on the received selectionof the multiple blocks, that represent the identified common entity orentities.

In another aspect, a non-transitory computer readable medium embodyinginstructions when executed by a processor to cause operations to beperformed for correlating application performance data with hardwareperformance data is disclosed. The operations include: receiving dataassociated with monitored applications and hardware; identifyingapplication performance data and hardware performance data from thereceived data; generating interactive heat maps of the applicationperformance data and interactive heat maps of the hardware performancedata; providing a user interface for displaying the generated heat maps;and displaying, through the user interface, the generated heat maps ofthe application performance data and the generated heat maps of thehardware performance data using a common time scale.

The non-transitory computer readable medium can be implemented invarious ways to include one or more of the following features. Forexample, generating the interactive heat maps can include: generatinginteractive heat maps for a specific application performance data metricor a specific hardware performance metric or both a specific applicationperformance data metric and a specific hardware performance metric. Thespecific application performance data metric can include call counts.The specific hardware performance data metric can include CPU usagepercentage, memory usage percentage, or both CPU usage percentage andmemory usage percentage. The operations can include: receiving aselection of a block from the application performance heat maps or ablock from the hardware performance heat maps; and responsive to thereceived selection of the block, displaying one or more blocks from thehardware performance heat maps or one or more blocks from theapplication performance heat maps, depending on the received selectionof the block, to visually indicate a correlation. The operations caninclude: applying highlights, colors, shading, brightness, transparency,or text to indicate the correlation between selected block and thecorrelated block or blocks. The operations can include: displayingadditional information to identify the selected block and the correlatedblock or blocks. The operations can include: receiving a selection ofmultiple blocks from the application performance heat maps or multipleblocks from the hardware performance heat maps; responsive to thereceived selection of the multiple blocks, identifying a common entityor entities that correlate to all of the selected multiple blocks; anddisplaying one or more blocks from the hardware performance heat maps orone or more blocks from the application performance heat maps, dependingon the received selection of the multiple blocks, that represent theidentified common entity or entities.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A and 1B are block diagrams of exemplary heat maps that providecorrelation between application performance and hardware performance viaheat maps as disclosed with respect to FIGS. 2A-2G.

FIGS. 2A-2G are process flow diagrams of exemplary processes forcorrelating application performance with hardware performance using heatmaps.

FIG. 3 is a block diagram of an exemplary application intelligenceplatform that can implement the correlation between applicationperformance and hardware performance via heat maps as disclosed in thispatent document.

FIG. 4 is a block diagram of an exemplary system for performing thecorrelation between application performance and hardware performance viaheat maps as disclosed in this patent document, including the processesdisclosed with respect to FIGS. 1A-1B and 2A-2G.

FIG. 5 is a block diagram of an exemplary computing system implementingthe disclosed technology.

DETAILED DESCRIPTION

The Internet and the World Wide Web have enabled the proliferation ofweb services available for virtually all types of businesses. Due to theaccompanying complexity of the infrastructure supporting the webservices, it is becoming increasingly difficult to maintain the highestlevel of service performance and user experience to keep up with theincrease in web services. For example, it can be challenging to piecetogether monitoring and logging data across disparate systems, tools,and layers in a network architecture. Moreover, even when data can beobtained, it is difficult to directly connect the chain of events andcause and effect.

To maintain the highest level of service performance and end userexperience, each web application can be monitored to provide insightinto information that can negatively affect the overall performance ofthe web application, which can cause negative end user experience. Inaddition, any correlation between the performance of the applicationsand the performance of associated hardware can provide crucialinformation on the cause and effect of detected anomalies.

Correlating Application Performance with Hardware Performance Via HeatMaps—Overview

In cloud environments or datacenters, a collection of compute nodes(e.g., nodes of servers) running application code exhibit similarcharacteristics, demonstrated either by application performance metricsor hardware performance metrics. A plot of the time-series of theapplication performance metric values and the hardware performancemetric values forms a band. Outliers on the plot are metric values thatdeviate so much that they lie out of the band and are easier to visuallydistinguish from band. Heat maps are used to spot such anomalous(outlier) behaviors while troubleshooting performance issues.

To further advance the troubleshooting process requires knowledge ofwhether underlying hardware performance is the root cause for the poorapplication performance. Users attempting to perform this analysis needto manually comparing metric values of each server one-by-one, orexamine each server and look for any suspicious indicators. This manualprocess is tedious, time consuming, and inefficient to be scalable.

The technology disclosed in this patent document provides for dynamicand efficient application intelligence platforms, systems, devices,methods, and computer readable media including non-transitory type thatembody instructions for causing a machine including a processor toperform various operations disclosed in this patent document tocorrelate application performance with hardware performance via heatmaps. The disclosed technology for using heat maps to show correlationbetween application performance and hardware performance can be used todisplay a single view of the correlation that enables a user to easilyunderstand the relationship between application performance and hardwareperformance by viewing the heat maps. The heat maps provide a correlateview of the application performance metrics and hardware infrastructuremetrics that makes it very easy to identify the infrastructure entitiesimpacting the performance of a given application.

The disclosed technology for correlating application performance withhardware performance using heat maps can provide a number of advantages.For example, the disclosed heat maps can enable a user to visuallyidentify the cause and effect of poor application performance usingmultiple heat maps. The disclosed technology avoids inefficiency andineffectiveness of manually review objects or entities in isolation. Thedisclosed technology for using heat maps to correlate applicationperformance with hardware performance can augment any presentation ofperformance data on reports and dashboards. When working in complexlarge environments and correlating application performance with hardwareperformance, providing a single view that illustrate the correlation ofapplication performance with hardware performance can instantly providevisual confirmation of the root cause of the detected anomalies. Thedisclosed technology for correlating application performance withhardware performance enables a new mechanism to present, analyze andextract valuable insight from huge dataset and pinpoint outliers.

Exemplary Heat Maps

FIGS. 1A-1B are block diagrams of exemplary heat maps 100 and 102 forcorrelating application performance with hardware performance. Thedisclosed technology provides interactive heat maps 100 and 102 of keyapplication performance metrics and corresponding hardware (e.g.server's) performance metrics. User can visually identify outlierapplication performance metrics and interact with the outlierapplication performance metrics to pinpoint the specific hardware (e.g.,servers) affecting the outlier application performance.

For example, heat maps 100 in FIG. 1A is displayed through a dashboarduser interface 101 that allows reports on performance anomaliesassociated with a monitored environment to be displayed to a user. Thedashboard interface can include a view 110 devoted to displaying theheat maps that correlate application performance metrics with hardwareperformance metrics. In the example shown in FIG. 1A, the heat maps 100include three different heat maps 112, 114, and 116. The topmost heatmap 112 (Tier Call Counts) illustrates application performance whereeach block of the heat map 112 represents a collection of nodes havingcall count value ‘y’ at time T. In other words, the y-axis representsthe tier call count values and the x-axis represents time. All nodesbelong to a particular tier ‘T’. The blocks in the heat map 112 aredisplayed using different density or darkness of color with the darkerblocks representing higher number of nodes with the call count value‘y’.

The middle heat map 114 (CPU %) illustrates hardware (e.g., server)performance metrics, such as the CPU %. In the hardware performance heatmap 114, each block represents a collection of hardware (e.g., servers).Each hardware maps to at least one node in heat map 112. In addition,each data point in heat map 114 represents mapping of servers to nodesand vice-versa.

The bottom heat map 116 (Memory %) illustrates hardware (e.g., server)performance metrics, such as the Memory %. In the hardware performanceheat map 116, each block represents a collection of hardware (e.g.,servers). Each hardware maps to at least one node in heat map 112. Inaddition, each data point in heat map 114 represents mapping of serversto nodes and vice-versa.

The blocks in the hardware performance heat maps 114 and 116 aredisplayed using different density or darkness of color with the darkerblocks representing higher number of servers exhibiting certain hardwarebehaviors, such as CPU usage being a certain % or % range or memoryconsumption being a certain % or % range. The y-axis represents thespecific behavior (CPU % in heat map 114 and Memory % in heat map 116)values and the x-axis represents time.

The application performance heat maps 112 is displayed with the sametime scale on the x-axis as the hardware performance heat maps 114 and116. The time axis alignment of the application performance heat maps112 with the hardware performance heat maps 114 and 116 enables theapplication performance metrics to be correlated at the selected timedimension (e.g., time slice) with the hardware performance metrics in asingle view.

In FIG. 1B, heat maps 102 is displayed to be substantially similar tothe heat maps 100. Heat maps 102 in FIG. 1B is provided to illustratethe correlation of the application performance metrics with the hardperformance metrics at the selected time dimension (e.g., time slice).In FIG. 1B, an outlier block 118 at 11:30 AM is illustrated to includenodes N1, N2, as an example. When a user interacts (e.g., mouse clicksor mouse hovers) with that block 118, the corresponding blocks 120 and122 in heat maps 114 and 116 respective at the same time 11:30 AM isillustrated to include servers S1 and S2. The correlated hardwareperformance metrics blocks are visually highlighted along with theapplication performance metrics block 118 to visually indicate thecorrelation at the selected time dimension (e.g., time slice). Differentvisual techniques can be used to highlight the correlated blocksincluding using colors, brightness, outlines, shading, shadows,transparency, etc. The heat maps 112, 114, and 116 show that server S1is associated with node N1 and server S2 is associated with N2. Inaddition, Nodes 1 and 2 experiencing call counts of nearly 80 arecorrelated to servers S1 and S2 experiencing nearly 100% CPU usage andnearly 40% memory usage at the selected time dimension (e.g., timeslice). Clearly, the application performance block 118 is an outlier.When either of the hardware performance blocks 120 and 122 are alsooutliers, then a determination can be made that the server performanceis likely the root cause of the poor application performance. In theexample shown in FIG. 1B, CPU % block 120 is an outlier and thus a userviewing the heat maps 112, 114, and 116 can determine that the poor CPUserver performance of servers S1 and S2 is likely the root cause of thepoor performance of nodes N1 and N2 in block 118.

In some implementations, the interactive heat maps can be configured toreceive user selection of multiple outlier blocks at multiple times t1,t2, t3. Response to receiving selection of multiple outlier blocks, thecommon entities in the multiple outlier blocks can be presented to theuser. Common entity identification can reveal any commonality in all ofthe outliers. Once the common entities are identified from the multipleoutlier blocks, troubleshooting can then focus on only those nodes orservers that are common to all three outlier blocks.

FIGS. 2A-2G are process flow diagrams of exemplary processes 200, 202,204, 206, and 208 for correlating application performance with hardwareperformance at the selected time dimension (e.g., time slice). Thevarious aspects of the processes 200, 202, 206, and 208 can be combinedtogether. As discussed further below with respect to FIGS. 3-5,performance issues with a monitored system of applications andassociated hardware are detected by monitoring application performanceand the associated hardware performance using agents. Agents can beinstalled at the servers running the applications, the infrastructuremachines, and at client side as needed. The monitored applications canbe performed by the servers distributed over a number of interconnectednodes. Each node can include one or more servers or machines thatperform part of the applications. The agents collect data associatedwith the applications of interest and associated nodes and machineswhere the applications are being operated. Examples of the collecteddata include performance data, such as metrics, metadata, and topologydata that indicate relationship information. A controller incommunication with the agents receive the data collected by the agents(210). The controller can be remotely located from the agents. Thecontroller analyzes the received data to identify applicationperformance data and hardware performance data (220). The controllergenerates interactive heat maps for the application performance data andheat maps for hardware performance data (230). A user interface (e.g.,an interactive dashboard) is provided for displaying the data receivedfrom the agents including the generated heat maps (240). Through theuser interface, the controller can display the generated interactiveheat maps as a single view that shows the correlation between theapplication performance and infrastructure or resource performance atthe selected time dimension (e.g., time slice) (250).

As shown in FIGS. 2B and 2C, the application performance heat maps canbe generated for a specific or each specific application performancemetric (e.g., call counts) (232). Similarly, the hardware performanceheat maps can be generated for a specific or each specific hardwareperformance metric (e.g., CPU %, Memory %) (234).

The processes 202 and 204 in FIGS. 2D and 2E are substantially similarto the process 200 except that processes 202 and 204 include additionalinteractive features of the heat maps. For example, user selection of ablock from the application performance heat maps or a block from thehardware performance heat maps is received (260). Responsive to thereceived selection of a block, one or more blocks from the hardwareperformance heat maps (when a block from the application performanceheat maps is selected) or one or more blocks from the applicationperformance heat maps (when a block from the hardware performance heatmaps is selected) are displayed to visually indicate a correlation withthe selected block at the selected time dimension (e.g., time slice)(270). Different display techniques, including colors, shading, fading,brightness, darkness, transparency, opaqueness, highlights, etc. can beused to indicate the correlation. In some implementations texts can bedisplayed to indicate the correlation. In addition, additionalinformation can be displayed for the selected block and the correlatedblock or blocks (280). For example, the identification of the nodes inthe application performance heat maps and the identification of thehardware in the hardware performance heat maps can be displayed. Usingthe displayed additional information, a user selecting a block in theapplication performance heat maps can understand exactly which node iscorrelated with which hardware, for example.

The processes 206 and 208 in FIGS. 2D and 2E are substantially similarto the processes 200, 202, and 204 except that processes 206 and 208include additional interactive features of the heat maps. For example,user selection of multiple blocks from the application performance heatmaps or multiple blocks from the hardware performance heat maps isreceived (262). Responsive to the received selection of the multipleblocks, common entity or entities that correlate to all of the selectedmultiple blocks (which represent a number of nodes) are identified(262). One or more blocks from the hardware performance heat maps (whenmultiple blocks from the application performance heat maps are selected)or one or more blocks from the application performance heat maps (whenmultiple blocks from the hardware performance heat maps are selected)that represent the identified common entity or entities are displayed tovisually indicate a correlation of the common entity or entities withthe selected multiple block at the selected time dimension (e.g., timeslice) (272). Different display techniques, including colors, shading,fading, brightness, darkness, transparency, opaqueness, highlights, etc.can be used to indicate the correlation. In some implementations textscan be displayed to indicate the correlation. In addition, additionalinformation can be displayed for the selected multiple blocks and thecorrelated block or blocks at the selected time dimension (e.g., timeslice) (282). For example, the identification of the nodes in theapplication performance heat maps and the identification of the hardwarein the hardware performance heat maps can be displayed. Using thedisplayed additional information, a user selecting multiple blocks inthe application performance heat maps can understand exactly which nodeis correlated with which hardware, for example. Also, the user can seewhich common entity or entities are correlated to all of the multipleblocks selected by the user at the selected time dimension (e.g., timeslice).

Application Intelligence Platform Architecture

FIG. 3 is a block diagram of an exemplary application intelligenceplatform 300 that can implement the correlating application performancedata with hardware performance data using heat maps at the selected timedimension (e.g., time slice) as disclosed in this patent document,including the processes disclosed with respect to FIGS. 1A-1B and 2A-2G.The application intelligence platform is a system that monitors andcollect metrics of performance data for an application environment beingmonitored. At the simplest structure, the application intelligenceplatform includes one or more agents 310, 312, 314, 316 and one or morecontrollers 320. While FIG. 3 shows four agents communicatively linkedto a single controller, the total number of agents and controller canvary based on a number of factors including the number of applicationsmonitored, how distributed the application environment is, the level ofmonitoring desired, the level of user experience desired, etc.

Controllers and Agents

The controller 320 is the central processing and administration serverfor application intelligence platform. The controller 320 serves abrowser-based user interface (UI) 330 that is the primary interface formonitoring, analyzing, and troubleshooting the monitored environment.The controller 320 can control and manage monitoring of businesstransactions distributed over application servers. Specifically, thecontroller 320 can receive runtime data from agents 310, 312, 314, 316and coordinators, associate portions of business transaction data,communicate with agents to configure collection of runtime data, andprovide performance data and reporting through the interface 330. Theinterface 330 may be viewed as a web-based interface viewable by aclient device 340. In some implementations, a client device 340 candirectly communicate with controller 320 to view an interface formonitoring data.

In the Software as a Service (SaaS) implementation, a controllerinstance 320 is hosted remotely by a provider of the applicationintelligence platform 300. In the on-premise (On-Prem) implementation, acontroller instance 320 is installed locally and self-administered.

The controllers 320 receive data from different agents 310, 312, 314,316 deployed to monitor applications, databases and database servers,servers, and end user clients for the monitored environment. Any of theagents 310, 312, 314, 316 can be implemented as different types ofagents specific monitoring duties. For example, application agents areinstalled on each server that hosts applications to be monitored.Instrumenting an agent adds an application agent into the runtimeprocess of the application.

Database agents are software (e.g., Java program) installed on a machinethat has network access to the monitored databases and the controller.Database agents queries the databases monitored to collect metrics andpasses the metrics for display in the metric browser—database monitoringand in the databases pages of the controller UI. Multiple databaseagents can report to the same controller. Additional database agents canbe implemented as backup database agents to take over for the primarydatabase agents during a failure or planned machine downtime. Theadditional database agents can run on the same machine as the primaryagents or on different machines. A database agent can be deployed ineach distinct network of the monitored environment. Multiple databaseagents can run under different user accounts on the same machine.

Standalone machine agents are standalone programs (e.g., standalone Javaprogram) that collect hardware-related performance statistics from theservers in the monitored environment. The standalone machine agents canbe deployed on machines that host application servers, database servers,messaging servers, Web servers, etc. A standalone machine agent has anextensible architecture.

End user monitoring (EUM) is performed using browser agents and mobileagents to provide performance information from the point of view of theclient, such as a web browser or a mobile native application. Browseragents and mobile agents are unlike other monitoring through applicationagents, database agents, and standalone machine agents that being on theserver. Through EUM, web use (e.g., by real users or synthetic agents),mobile use, or any combination can be monitored depending on themonitoring needs. Browser agents (e.g., agents 310, 312, 314, 316) caninclude Reporters that perform the automatic webpage loading detectionas disclosed in this patent document.

Browser agents are small files using web-based technologies, such asJavaScript agents injected into each instrumented web page, as close tothe top as possible, as the web page is served and collects data. Oncethe web page has completed loading, the collected data is bundled into abeacon and sent to the EUM cloud for processing and ready for retrievalby the controller. Browser real user monitoring (Browser RUM) providesinsights into the performance of a web application from the point ofview of a real or synthetic end user. For example, Browser RUM candetermine how specific Ajax or iframe calls are slowing down page loadtime and how server performance impact end user experience in aggregateor in individual cases.

A mobile agent is a small piece of highly performant code that getsadded to the source of the mobile application. Mobile RUM providesinformation on the native iOS or Android mobile application as the endusers actually use the mobile application. Mobile RUM providesvisibility into the functioning of the mobile application itself and themobile application's interaction with the network used and anyserver-side applications the mobile application communicates with.

The controller 320 can include a correlation system 350 for correlatingapplication performance data with hardware performance data using heatmaps at the selected time dimension (e.g., time slice) as disclosed inthis patent document. In some implementations, the correlation system350 can be implemented in a separate machine (e.g., a server) differentfrom the one hosting the controller 320.

Application Intelligence Monitoring

The disclosed technology can provide application intelligence data bymonitoring an application environment that includes various servicessuch as web applications served from an application server (e.g., Javavirtual machine (JVM), Internet Information Services (IIS), HypertextPreprocessor (PHP) Web server, etc.), databases or other data stores,and remote services such as message queues and caches. The services inthe application environment can interact in various ways to provide aset of cohesive user interactions with the application, such as a set ofuser services applicable to end user customers.

Application Intelligence Modeling

Entities in the application environment (such as the JBoss service,MQSeries modules, and databases) and the services provided by theentities (such as a login transaction, service or product search, orpurchase transaction) are mapped to an application intelligence model.In the application intelligence model, a business transaction representsa particular service provided by the monitored environment. For example,in an e-commerce application, particular real-world services can includeuser logging in, searching for items, or adding items to the cart. In acontent portal, particular real-world services can include user requestsfor content such as sports, business, or entertainment news. In a stocktrading application, particular real-world services can includeoperations such as receiving a stock quote, buying, or selling stocks.

Business Transactions

A business transaction representation of the particular service providedby the monitored environment provides a view on performance data in thecontext of the various tiers that participate in processing a particularrequest. A business transaction represents the end-to-end processingpath used to fulfill a service request in the monitored environment.Thus, a business environment is a type of user-initiated action in themonitored environment defined by an entry point and a processing pathacross application servers, databases, and potentially many otherinfrastructure components. Each instance of a business transaction is anexecution of that transaction in response to a particular user request.A business transaction can be created by detecting incoming requests atan entry point and tracking the activity associated with request at theoriginating tier and across distributed components in the applicationenvironment. A flow map can be generated for a business transaction thatshows the touch points for the business transaction in the applicationenvironment.

Performance monitoring can be oriented by business transaction to focuson the performance of the services in the application environment fromthe perspective of end users. Performance monitoring based on businesstransaction can provide information on whether a service s available(e.g., users can log in, check out, or view their data), response timesfor users, and the cause of problems when the problems occur.

Business Applications

A business application is the top-level container in the applicationintelligence model. A business application contains a set of relatedservices and business transactions. In some implementations, a singlebusiness application may be needed to model the environment. In someimplementations, the application intelligence model of the applicationenvironment can be divided into several business applications. Businessapplications can be organized differently based on the specifics of theapplication environment. One consideration is to organize the businessapplications in a way that reflects work teams in a particularorganization, since role-based access controls in the Controller UI areoriented by business application.

Nodes

A node in the application intelligence model corresponds to a monitoredserver or JVM in the application environment. A node is the smallestunit of the modeled environment. In general, a node corresponds to anindividual application server, JVM, or CLR on which a monitoring Agentis installed. Each node identifies itself in the applicationintelligence model. The Agent installed at the node is configured tospecify the name of the node, tier, and business application under whichthe Agent reports data to the Controller.

Tiers

Business applications contain tiers, the unit in the applicationintelligence model that includes one or more nodes. Each node representsan instrumented service (such as a web application). While a node can bea distinct application in the application environment, in theapplication intelligence model, a node is a member of a tier, which,along with possibly many other tiers, make up the overall logicalbusiness application.

Tiers can be organized in the application intelligence model dependingon a mental model of the monitored application environment. For example,identical nodes can be grouped into a single tier (such as a cluster ofredundant servers). In some implementations, any set of nodes, identicalor not, can be grouped for the purpose of treating certain performancemetrics as a unit into a single tier.

The traffic in a business application flows among tiers and can bevisualized in a flow map using lines among tiers. In addition, the linesindicating the traffic flows among tiers can be annotated withperformance metrics. In the application intelligence model, there maynot be any interaction among nodes within a single tier. Also, in someimplementations, an application agent node cannot belong to more thanone tier. Similarly, a machine agent cannot belong to more than onetier. However, more than one machine agent can be installed on amachine.

Backend System

A backend is a component that participates in the processing of abusiness transaction instance. A backend is not instrumented by anagent. A backend may be a web server, database, message queue, or othertype of service. The agent recognizes calls to these backend servicesfrom instrumented code (called exit calls). When a service is notinstrumented and cannot continue the transaction context of the call,the agent determines that the service is a backend component. The agentpicks up the transaction context at the response at the backend andcontinues to follow the context of the transaction from there.

Performance information is available for the backend call. For detailedtransaction analysis for the leg of a transaction processed by thebackend, the database, web service, or other application need to beinstrumented.

Baselines and Thresholds

The application intelligence platform uses both self-learned baselinesand configurable thresholds to help identify application issues. Acomplex distributed application has a large number of performancemetrics and each metric is important in one or more contexts. In suchenvironments, it is difficult to determine the values or ranges that arenormal for a particular metric; set meaningful thresholds on which tobase and receive relevant alerts; and determine what is a “normal”metric when the application or infrastructure undergoes change. Forthese reasons, the disclosed application intelligence platform canperform anomaly detection based on dynamic baselines or thresholds.

The disclosed application intelligence platform automatically calculatesdynamic baselines for the monitored metrics, defining what is “normal”for each metric based on actual usage. The application intelligenceplatform uses these baselines to identify subsequent metrics whosevalues fall out of this normal range. Static thresholds that are tediousto set up and, in rapidly changing application environments,error-prone, are no longer needed.

The disclosed application intelligence platform can use configurablethresholds to maintain service level agreements (SLAs) and ensureoptimum performance levels for your system by detecting slow, very slow,and stalled transactions. Configurable thresholds provide a flexible wayto associate the right business context with a slow request to isolatethe root cause.

Health Rules, Policies, and Actions

In addition, health rules can be set up with conditions that use thedynamically generated baselines to trigger alerts or initiate othertypes of remedial actions when performance problems are occurring or maybe about to occur.

For example, dynamic baselines can be used to automatically establishwhat is considered normal behavior for a particular application.Policies and health rules can be used against baselines or other healthindicators for a particular application to detect and troubleshootproblems before users are affected, Health rules can be used to definemetric conditions to monitor, such as when the “average response time isfour times slower than the baseline”. The health rules can be createdand modified based on the monitored application environment.

Examples of health rules for testing business transaction performancecan include business transaction response time and business transactionerror rate. For example, health rule that tests whether the businesstransaction response time is much higher than normal can define acritical condition as the combination of an average response timegreater than the default baseline by 3 standard deviations and a loadgreater than 50 calls per minute. This health rule can define a warningcondition as the combination of an average response time greater thanthe default baseline by 2 standard deviations and a load greater than100 calls per minute. The health rule that tests whether the businesstransaction error rate is much higher than normal can define a criticalcondition as the combination of an error rate greater than the defaultbaseline by 3 standard deviations and an error rate greater than 10errors per minute and a load greater than 50 calls per minute. Thishealth rule can define a warning condition as the combination of anerror rate greater than the default baseline by 2 standard deviationsand an error rate greater than 5 errors per minute and a load greaterthan 50 calls per minute.

Policies can be configured to trigger actions when a health rule isviolated or when any event occurs. Triggered actions can includenotifications, diagnostic actions, auto-scaling capacity, runningremediation scripts.

Metrics

Most of the metrics relate to the overall performance of the applicationor business transaction (e.g., load, average response time, error rate,etc.) or of the application server infrastructure (e.g., percentage CPUbusy, percentage of memory used, etc.). The Metric Browser in thecontroller UI can be used to view all of the metrics that the agentsreport to the controller.

In addition, special metrics called information points can be created toreport on how a given business (as opposed to a given application) isperforming. For example, the performance of the total revenue for acertain product or set of products can be monitored. Also, informationpoints can be used to report on how a given code is performing, forexample how many times a specific method is called and how long it istaking to execute. Moreover, extensions that use the machine agent canbe created to report user defined custom metrics. These custom metricsare base-lined and reported in the controller, just like the built-inmetrics.

All metrics can be accessed programmatically using a RepresentationalState Transfer (REST) API that returns either the JavaScript ObjectNotation (JSON) or the eXtensible Markup Language (XML) format. Also,the REST API can be used to query and manipulate the applicationenvironment.

Snapshots

Snapshots provide a detailed picture of a given application at a certainpoint in time. Snapshots usually include call graphs that allow thatenables drilling down to the line of code that may be causingperformance problems. The most common snapshots are transactionsnapshots.

Exemplary Implementation of Application Intelligence Platform

FIG. 4 is a block diagram of an exemplary system 400 for performingcorrelation of application performance data with hardware performancedata using heat maps at the selected time dimension (e.g., time slice)as disclosed in this patent document, including the processes disclosedwith respect to FIGS. 1A-1B and 2A-2G. The system 400 in FIG. 4 includesclient device 405 and 492, mobile device 415, network 420, networkserver 425, application servers 430, 440, 450 and 460, asynchronousnetwork machine 470, data stores 480 and 485, controller 490, and datacollection server 495. The controller 490 can include correlation system496 for correlating application performance data with hardwareperformance data using heat maps as disclosed in this patent document.In some implementations, the correlation system 496 can be implementedin a separate machine (e.g., a server) different from the one hostingthe controller 490.

Client device 405 may include network browser 410 and be implemented asa computing device, such as for example a laptop, desktop, workstation,or some other computing device. Network browser 410 may be a clientapplication for viewing content provided by an application server, suchas application server 430 via network server 425 over network 420.

Network browser 410 may include agent 412. Agent 412 may be installed onnetwork browser 410 and/or client 405 as a network browser add-on,downloading the application to the server, or in some other manner.Agent 412 may be executed to monitor network browser 410, the operatingsystem of client 405, and any other application, API, or anothercomponent of client 405. Agent 412 may determine network browsernavigation timing metrics, access browser cookies, monitor code, andtransmit data to data collection 460, controller 490, or another device.Agent 412 may perform other operations related to monitoring a requestor a network at client 405 as discussed herein including a Report forautomatic detection of webpage loading and report generating.

Mobile device 415 is connected to network 420 and may be implemented asa portable device suitable for sending and receiving content over anetwork, such as for example a mobile phone, smart phone, tabletcomputer, or another portable device. Both client device 405 and mobiledevice 415 may include hardware and/or software configured to access aweb service provided by network server 425.

Mobile device 415 may include network browser 417 and an agent 419.Mobile device may also include client applications and other code thatmay be monitored by agent 419. Agent 419 may reside in and/orcommunicate with network browser 417, as well as communicate with otherapplications, an operating system, APIs and other hardware and softwareon mobile device 415. Agent 419 may have similar functionality as thatdescribed herein for agent 412 on client 405, and may repot data to datacollection server 460 and/or controller 490.

Network 420 may facilitate communication of data among differentservers, devices and machines of system 400 (some connections shown withlines to network 420, some not shown). The network may be implemented asa private network, public network, intranet, the Internet, a cellularnetwork, Wi-Fi network, VoIP network, or a combination of one or more ofthese networks. The network 420 may include one or more machines such asload balance machines and other machines.

Network server 425 is connected to network 420 and may receive andprocess requests received over network 420. Network server 425 may beimplemented as one or more servers implementing a network service, andmay be implemented on the same machine as application server 430 or oneor more separate machines. When network 420 is the Internet, networkserver 425 may be implemented as a web server.

Application server 430 communicates with network server 425, applicationservers 440 and 450, and controller 490. Application server 450 may alsocommunicate with other machines and devices (not illustrated in FIG. 4).Application server 430 may host an application or portions of adistributed application. The host application 432 may be in one of manyplatforms, such as including a Java, PHP, .Net, and Node.JS, beimplemented as a Java virtual machine, or include some other host type.Application server 430 may also include one or more agents 434 (i.e.“modules”), including a language agent, machine agent, and networkagent, and other software modules. Application server 430 may beimplemented as one server or multiple servers as illustrated in FIG. 4.

Application 432 and other software on application server 430 may beinstrumented using byte code insertion, or byte code instrumentation(BCI), to modify the object code of the application or other software.The instrumented object code may include code used to detect callsreceived by application 432, calls sent by application 432, andcommunicate with agent 434 during execution of the application. BCI mayalso be used to monitor one or more sockets of the application and/orapplication server in order to monitor the socket and capture packetscoming over the socket.

In some embodiments, server 430 may include applications and/or codeother than a virtual machine. For example, servers 430, 440, 450, and460 may each include Java code, .Net code, PHP code, Ruby code, C code,C++ or other binary code to implement applications and process requestsreceived from a remote source. References to a virtual machine withrespect to an application server are intended to be for exemplarypurposes only.

Agents 434 on application server 430 may be installed, downloaded,embedded, or otherwise provided on application server 430. For example,agents 434 may be provided in server 430 by instrumentation of objectcode, downloading the agents to the server, or in some other manner.Agent 434 may be executed to monitor application server 430, monitorcode running in a virtual machine 432 (or other program language, suchas a PHP, .Net, or C program), machine resources, network layer data,and communicate with byte instrumented code on application server 430and one or more applications on application server 430.

Each of agents 434, 444, 454 and 464 may include one or more agents,such as language agents, machine agents, and network agents. A languageagent may be a type of agent that is suitable to run on a particularhost. Examples of language agents include a JAVA agent, .Net agent, PHPagent, and other agents. The machine agent may collect data from aparticular machine on which it is installed. A network agent may capturenetwork information, such as data collected from a socket.

Agent 434 may detect operations such as receiving calls and sendingrequests by application server 430, resource usage, and incomingpackets. Agent 434 may receive data, process the data, for example byaggregating data into metrics, and transmit the data and/or metrics tocontroller 490. Agent 434 may perform other operations related tomonitoring applications and application server 430 as discussed herein.For example, agent 434 may identify other applications, share businesstransaction data, aggregate detected runtime data, and other operations.

An agent may operate to monitor a node, tier or nodes or other entity. Anode may be a software program or a hardware component (e.g., memory,processor, and so on). A tier of nodes may include a plurality of nodeswhich may process a similar business transaction, may be located on thesame server, may be associated with each other in some other way, or maynot be associated with each other.

A language agent may be an agent suitable to instrument or modify,collect data from, and reside on a host. The host may be a Java, PHP,.Net, Node.JS, or other type of platform. Language agent may collectflow data as well as data associated with the execution of a particularapplication. The language agent may instrument the lowest level of theapplication to gather the flow data. The flow data may indicate whichtier is communicating with which tier and on which port. In someinstances, the flow data collected from the language agent includes asource IP, a source port, a destination IP, and a destination port. Thelanguage agent may report the application data and call chain data to acontroller. The language agent may report the collected flow dataassociated with a particular application to a network agent.

A network agent may be a standalone agent that resides on the host andcollects network flow group data. The network flow group data mayinclude a source IP, destination port, destination IP, and protocolinformation for network flow received by an application on which networkagent is installed. The network agent may collect data by interceptingand performing packet capture on packets coming in from a one or moresockets. The network agent may receive flow data from a language agentthat is associated with applications to be monitored. For flows in theflow group data that match flow data provided by the language agent, thenetwork agent rolls up the flow data to determine metrics such as TCPthroughput, TCP loss, latency and bandwidth. The network agent may thenreport the metrics, flow group data, and call chain data to acontroller. The network agent may also make system calls at anapplication server to determine system information, such as for examplea host status check, a network status check, socket status, and otherinformation.

A machine agent may reside on the host and collect information regardingthe machine which implements the host. A machine agent may collect andgenerate metrics from information such as processor usage, memory usage,and other hardware information.

Each of the language agent, network agent, and machine agent may reportdata to the controller. Controller 490 may be implemented as a remoteserver that communicates with agents located on one or more servers ormachines. The controller may receive metrics, call chain data and otherdata, correlate the received data as part of a distributed transaction,and report the correlated data in the context of a distributedapplication implemented by one or more monitored applications andoccurring over one or more monitored networks. The controller mayprovide reports, one or more user interfaces, and other information fora user.

Agent 434 may create a request identifier for a request received byserver 430 (for example, a request received by a client 405 or 415associated with a user or another source). The request identifier may besent to client 405 or mobile device 415, whichever device sent therequest. In embodiments, the request identifier may be created when adata is collected and analyzed for a particular business transaction.

Each of application servers 440, 450 and 460 may include an applicationand agents. Each application may run on the corresponding applicationserver. Each of applications 442, 452 and 462 on application servers440-460 may operate similarly to application 432 and perform at least aportion of a distributed business transaction. Agents 444, 454 and 464may monitor applications 442-462, collect and process data at runtime,and communicate with controller 490. The applications 432, 442, 452 and462 may communicate with each other as part of performing a distributedtransaction. In particular, each application may call any application ormethod of another virtual machine.

Asynchronous network machine 470 may engage in asynchronouscommunications with one or more application servers, such as applicationserver 450 and 460. For example, application server 450 may transmitseveral calls or messages to an asynchronous network machine. Ratherthan communicate back to application server 450, the asynchronousnetwork machine may process the messages and eventually provide aresponse, such as a processed message, to application server 460.Because there is no return message from the asynchronous network machineto application server 450, the communications among them areasynchronous.

Data stores 480 and 485 may each be accessed by application servers suchas application server 450. Data store 485 may also be accessed byapplication server 450. Each of data stores 480 and 485 may store data,process data, and return queries received from an application server.Each of data stores 480 and 485 may or may not include an agent.

Controller 490 may control and manage monitoring of businesstransactions distributed over application servers 430-460. In someembodiments, controller 490 may receive application data, including dataassociated with monitoring client requests at client 405 and mobiledevice 415, from data collection server 460. In some embodiments,controller 490 may receive application monitoring data and network datafrom each of agents 412, 419, 434, 444 and 454. Controller 490 mayassociate portions of business transaction data, communicate with agentsto configure collection of data, and provide performance data andreporting through an interface. The interface may be viewed as aweb-based interface viewable by client device 492, which may be a mobiledevice, client device, or any other platform for viewing an interfaceprovided by controller 490. In some embodiments, a client device 492 maydirectly communicate with controller 490 to view an interface formonitoring data.

Client device 492 may include any computing device, including a mobiledevice or a client computer such as a desktop, work station or anothercomputing device. Client computer 492 may communicate with controller490 to create and view a custom interface. In some embodiments,controller 490 provides an interface for creating and viewing the custominterface as a content page, e.g., a web page, which may be provided toand rendered through a network browser application on client device 492.

Applications 432, 442, 452 and 462 may be any of several types ofapplications. Examples of applications that may implement applications432-462 include a Java, PHP, .Net, Node.JS, and other applications.

FIG. 5 is a block diagram of a computer system 500 for implementing thepresent technology. System 500 of FIG. 5 may be implemented in thecontexts of the likes of clients 405, 492, network server 425, servers430, 440, 450, 460, a synchronous network machine 470 and controller490.

The computing system 500 of FIG. 5 includes one or more processors 510and memory 520. Main memory 520 stores, in part, instructions and datafor execution by processor 510. Main memory 510 can store the executablecode when in operation. The system 500 of FIG. 5 further includes a massstorage device 530, portable storage medium drive(s) 540, output devices550, user input devices 560, a graphics display 570, and peripheraldevices 580.

The components shown in FIG. 5 are depicted as being connected via asingle bus 590. However, the components may be connected through one ormore data transport means. For example, processor unit 510 and mainmemory 520 may be connected via a local microprocessor bus, and the massstorage device 530, peripheral device(s) 580, portable or remote storagedevice 540, and display system 570 may be connected via one or moreinput/output (I/O) buses.

Mass storage device 530, which may be implemented with a magnetic diskdrive or an optical disk drive, is a non-volatile storage device forstoring data and instructions for use by processor unit 510. Massstorage device 530 can store the system software for implementingembodiments of the present invention for purposes of loading thatsoftware into main memory 620.

Portable storage device 540 operates in conjunction with a portablenon-volatile storage medium, such as a compact disk, digital video disk,magnetic disk, flash storage, etc. to input and output data and code toand from the computer system 500 of FIG. 5. The system software forimplementing embodiments of the present invention may be stored on sucha portable medium and input to the computer system 500 via the portablestorage device 540.

Input devices 560 provide a portion of a user interface. Input devices560 may include an alpha-numeric keypad, such as a keyboard, forinputting alpha-numeric and other information, or a pointing device,such as a mouse, a trackball, stylus, or cursor direction keys.Additionally, the system 500 as shown in FIG. 5 includes output devices550. Examples of suitable output devices include speakers, printers,network interfaces, and monitors.

Display system 570 may include a liquid crystal display (LCD) or anothersuitable display device. Display system 570 receives textual andgraphical information, and processes the information for output to thedisplay device.

Peripherals 580 may include any type of computer support device to addadditional functionality to the computer system. For example, peripheraldevice(s) 580 may include a modem or a router.

The components contained in the computer system 500 of FIG. 5 caninclude a personal computer, hand held computing device, telephone,mobile computing device, workstation, server, minicomputer, mainframecomputer, or any other computing device. The computer can also includedifferent bus configurations, networked platforms, multi-processorplatforms, etc. Various operating systems can be used including Unix,Linux, Windows, Apple OS, and other suitable operating systems,including mobile versions.

When implementing a mobile device such as smart phone or tabletcomputer, the computer system 500 of FIG. 5 may include one or moreantennas, radios, and other circuitry for communicating over wirelesssignals, such as for example communication using Wi-Fi, cellular, orother wireless signals.

While this patent document contains many specifics, these should not beconstrued as limitations on the scope of any invention or of what may beclaimed, but rather as descriptions of features that may be specific toparticular embodiments of particular inventions. Certain features thatare described in this patent document in the context of separateembodiments can also be implemented in combination in a singleembodiment. Conversely, various features that are described in thecontext of a single embodiment can also be implemented in multipleembodiments separately or in any suitable subcombination. Moreover,although features may be described above as acting in certaincombinations and even initially claimed as such, one or more featuresfrom a claimed combination can in some cases be excised from thecombination, and the claimed combination may be directed to asubcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. Moreover, the separation of various system components in theembodiments described in this patent document should not be understoodas requiring such separation in all embodiments.

Only a few implementations and examples are described and otherimplementations, enhancements and variations can be made based on whatis described and illustrated in this patent document.

What is claimed is:
 1. A system for correlating application performancedata with hardware performance data, the system including: a processor;a memory; and one or more modules stored in the memory and executable bya processor to perform operations including: receive data associatedwith monitored applications and hardware configured to perform amonitored business transaction; identify application performance dataand hardware performance data associated with the monitored businesstransaction from the received data; generate interactive heat maps ofthe application performance data and interactive heat maps of thehardware performance data, wherein, in a correlated view, a mapping ofthe interactive heat maps of the application performance data to theinteractive heat maps of the hardware performance data is shown as acorrelation between application performance and hardware performance;provide a user interface for displaying the generated heat maps;display, through the user interface, the generated heat maps of theapplication performance data and the generated heat maps of the hardwareperformance data using a common time scale; receive, through the userinterface, a selection of multiple blocks from the applicationperformance heat maps or the hardware performance heat maps; responsiveto the received selection of the multiple blocks, identify one or morecommon entities that correlate to the selection of the multiple blocks,the common entities being from the hardware configured to perform themonitored business transaction; and display, through the user interface,one or more blocks from the hardware performance heat maps or theapplication performance heat maps based on the received selection of themultiple blocks that represent the identified one or more commonentities.
 2. The system of claim 1, wherein when generating theinteractive heat maps, the one or more modules are executable to performoperations including: generate interactive heat maps for a specificapplication performance data metric or a specific hardware performancemetric or both a specific application performance data metric and aspecific hardware performance metric.
 3. The system of claim 2, whereinthe specific application performance data metric includes call counts.4. The system of claim 2, wherein the specific hardware performance datametric includes CPU usage percentage, memory usage percentage, or bothCPU usage percentage and memory usage percentage.
 5. The system of claim1, wherein the one or more modules are executable to perform operationsincluding: receive a selection of a block from the applicationperformance heat maps or the hardware performance heat maps; andresponsive to the received selection of the block, display one or moreblocks from the hardware performance heat maps or one or the applicationperformance heat maps based on the received selection of the block tovisually indicate the correlation.
 6. The system of claim 5, wherein theone or more modules are executable to visually indicate the correlationby performing operations including: apply highlights, colors, shading,brightness, transparency, or text to indicate the correlation betweenthe selection of the block and the one or more or blocks.
 7. The systemof claim 5, wherein the one or more modules are executable to performoperations including: display additional information to identify theselection of the block and the one or more correlated blocks.
 8. Amethod for correlating application performance data with hardwareperformance data, the method including: receiving data associated withmonitored applications and hardware configured to perform a monitoredbusiness transaction; identifying application performance data andhardware performance data associated with the monitored businesstransaction from the received data; generating interactive heat maps ofthe application performance data and interactive heat maps of thehardware performance data, wherein, in a correlated view, a mapping ofthe interactive heat maps of the application performance data to theinteractive heat maps of the hardware performance data is shown as acorrelation between application performance and hardware performance;providing a user interface for displaying the generated heat maps;displaying, through the user interface, the generated heat maps of theapplication performance data and the generated heat maps of the hardwareperformance data using a common time scale; receiving, through the userinterface, a selection of multiple blocks from the applicationperformance heat maps or the hardware performance heat maps; responsiveto receiving the selection of the multiple blocks, identifying one ormore common entities that correlate to the selection of the multipleblocks, the common entities being from the hardware configured toperform the monitored business transaction; and displaying, through theuser interface, one or more blocks from the hardware performance heatmaps or the application performance heat maps based on the receivedselection of the multiple blocks that represent the identified one ormore common entities.
 9. The method of claim 8, wherein generating theinteractive heat maps includes: generating interactive heat maps for aspecific application performance data metric or a specific hardwareperformance metric or both a specific application performance datametric and a specific hardware performance metric.
 10. The method ofclaim 9, wherein the specific application performance data metricincludes call counts.
 11. The method of claim 9, wherein the specifichardware performance data metric includes CPU usage percentage, memoryusage percentage, or both CPU usage percentage and memory usagepercentage.
 12. The method of claim 8, including: receiving a selectionof a block from the application performance heat maps or the hardwareperformance heat maps; and responsive to receiving the selection of theblock, displaying one or more blocks from the hardware performance heatmaps or one or the application performance heat maps based on thereceived selection of the block to visually indicate the correlation.13. The method of claim 12, including: applying highlights, colors,shading, brightness, transparency, or text to indicate the correlationbetween the selection of the block and the one or more correlatedblocks.
 14. The method of claim 12, including: displaying additionalinformation to identify the selection of the block and the one or morecorrelated blocks.
 15. A non-transitory computer readable mediumembodying instructions when executed by a processor to cause operationsto be performed for correlating application performance data withhardware performance data, the operations including: receiving dataassociated with monitored applications and hardware configured toperform a monitored business transaction; identifying applicationperformance data and hardware performance data associated with themonitored business transaction from the received data; generatinginteractive heat maps of the application performance data andinteractive heat maps of the hardware performance data, wherein, in acorrelated view, a mapping of the interactive heat maps of theapplication performance data to the interactive heat maps of thehardware performance data is shown as a correlation between applicationperformance and hardware performance; providing a user interface fordisplaying the generated heat maps; displaying, through the userinterface, the generated heat maps of the application performance dataand the generated heat maps of the hardware performance data using acommon time scale; receiving, through the user interface, a selection ofmultiple blocks from the application performance heat maps or thehardware performance heat maps; responsive to receiving the selection ofthe multiple blocks, identifying one or more common entities thatcorrelate to the selection of the multiple blocks, the common entitiesbeing from the hardware configured to perform the monitored businesstransaction; and displaying, through the user interface, one or moreblocks from the hardware performance heat maps or the applicationperformance heat maps based on the received selection of the multipleblocks that represent the identified one or more common entities. 16.The non-transitory computer readable medium of claim 15, whereingenerating the interactive heat maps includes: generating interactiveheat maps for a specific application performance data metric or aspecific hardware performance metric or both a specific applicationperformance data metric and a specific hardware performance metric. 17.The non-transitory computer readable medium of claim 16, wherein thespecific application performance data metric includes call counts. 18.The non-transitory computer readable medium of claim 16, wherein thespecific hardware performance data metric includes CPU usage percentage,memory usage percentage, or both CPU usage percentage and memory usagepercentage.
 19. The non-transitory computer readable medium of claim 15,wherein the operations include: receiving a selection of a block fromthe application performance heat maps or the hardware performance heatmaps; and responsive to receiving the selection of the block, displayingone or more blocks from the hardware performance heat maps or one or theapplication performance heat maps based on the received selection of theblock to visually indicate the correlation.
 20. The non-transitorycomputer readable medium of claim 19, wherein the operations include:applying highlights, colors, shading, brightness, transparency, or textto indicate the correlation between the selection of the block and theone or more correlated blocks.
 21. The non-transitory computer readablemedium of claim 19, wherein the operations include: displayingadditional information to identify the selection of the block and theone or more correlated blocks.